Special Symposium

Program Schedule

Please click here for a detailed program agenda

Travel Grant

Thank you for your submissions to the Symposium for Advanced manufacturing. All the papers were peer reviewed by experts in the field. We are glad to announce the winners of the travel grants. Based on the review scores, the following papers were chosen:

  • Bayesian Optimization for Predicting Rare Internal Failures in Manufacturing Processes (Abhinav Maurya)
  • Using Big Data to enhance the Bosch Production Line Performance: A Kaggle Challenge (Ankita Mangal, Nishant Kumar)
  • Machine Learning, Linear and Bayesian Models for Logistic Regression in the Failure Detection Problems (Bohdan Pavlyshenko)

  • Abhinav Maurya, Ankita Mangal and Bohdan Pavlyshenko have filled out the application form for the travel grants and would be receiving the travel grant of $2000 each.

Symposium on Data Analytics for Advanced Manufacturing

Theme: From Sensing to Decision-Making

Materials for Advancing AM through visual data science

Materials for MfgUSA-The National Network for Manufacturing Innovation

Dec.5-8, 2016 Washington D.C., USA


The manufacturing industry, which is vital to all economies, is being challenged to improve its efficiency across the product life cycle and the value chain. The critical aspect of "smartness" is enabled by advanced data analytics for understanding, prediction, and control of manufacturing systems across the product lifecycle (design analytics, production analytics, and use and post-use analytics) and to the extended networked enterprises. Continuous improvements in sensor technologies, data acquisition systems, and data mining and big data analytics allow the manufacturing industry to effectively and efficiently collect large, rapid, and diverse volumes of data and get valuable insights from this data. Big data analytics is becoming a key competitive differentiator having great potential for converting raw data into information assets for smarter decision making during design, manufacturing, use and post use.

In addition, there is a need for open standards, communication protocols, and high performance distributed computing. This symposium intends to provide a platform for researchers and industry practitioners from manufacturing, information science, and data science disciplines to share their data mining and big-data-analytics-related research results, and practical design or development experiences in the manufacturing industry. It will foster collaboration among academia, industry, and governmental organizations to help improve efficiency, product quality, and sustainability in small and large manufacturing industries.

Research Topics:

Topics covered by the symposium include, but are not restricted to, the following:

    Infrastructure and algorithms
  • Data systems architecture for a digital factory including sensors, security, and the Internet of Things (IoT)
  • Collecting, storing, retrieving, and pre-processing big data
  • New techniques for building predictive models from data
  • Modeling and simulation of manufacturing systems and operations for data analytics
  • Application of data mining and big data analytics to improve efficiency and productivity of manufacturing processes, and product quality
  • Prognostics and health management for manufacturing systems
  • Forecasting and predictive maintenance based on customer data
  • Case studies, surveys and reports on the impact of data mining and big data analytics in the manufacturing industry
    Standards and protocols
  • Standards and protocols for deployment and exchange of analytics solutions: PMML, PFA, MTConnect, CRISP-DM, etc.
  • Verification & validation issues related to application of DA in manufacturing
  • Techniques for quantifying uncertainty in data and models, and variability in physical processes

This symposium will consist of four sessions:

  1. Research Track: New research ideas on the development and application of data analytics techniques
  2. Industry Track: Current use of data analytics applied to solve real problems in the industry
  3. Keynote Speeches: Invited talks given by leading practitioners and domain experts in industry, government, and academia
  4. Manufacturing Data Challenge: A challenge problem for applying DA in manufacturing will be provided, and the best solutions from participants will be selected for presentation.

Important dates:

Sept 30, 2016: Results due for the manufacturing data challenge
Sept 30, 2016: Extended due date for full papers submission
Oct 20, 2016: Notification of paper acceptance
Nov 5, 2016: Camera-ready of accepted papers
Dec 5-8, 2016: Symposium

Symposium Organizers:

Dr. Sudarsan Rachuri
Advanced Manufacturing Office
Office of Energy Efficiency and Renewable Energy (EERE)
Department of Energy
E-mail: sudarsan@hq.doe.gov
Phone: +1-202-287-5943

Tina Lee
Systems Integration Division
National Institute of Standards and Technology
Gaithersburg, MD 20899
E-mail: yung-tsun.lee@nist.gov
Phone: +1-301 975 3550

Dr. Ronay Ak
Systems Integration Division
National Institute of Standards and Technology
Gaithersburg, MD 20899
E-mail: ronay.ak@nist.gov
Phone: +1-301 975 8655

Dr. Anantha Narayanan
Department of Mechanical Engineering
University of Maryland
College Park, MD, 20742
Email: anantha@umd.edu
Phone: +1-301 975 4322

Dr. Soundar Srinivasan
Robert Bosch, LLC
4005 Miranda Ave #200
Palo Alto, CA 94304
E-mail: Soundar.Srinivasan@us.bosch.com
Phone: +1-650 320 2980

Dr. Rumi Ghosh
Robert Bosch, LLC
4005 Miranda Ave #200
Palo Alto, CA 94304
E-mail: Rumi.Ghosh@us.bosch.com
Phone: +1-650 565 7459

Dr. Steve Eglash
Stanford Data Science Initiative
Stanford University
Stanford, CA 94305
E-mail: seglash@stanford.edu
Phone: +1-650 721 1637

For Paper Submission, please click here

Manufacturing Data Challenge

please visit here to access Manufacturing Data Challenge